Attitudes, intentions and preferences for using physical activity tracking devices

2015 ◽  
Vol 19 ◽  
pp. e44-e45
Author(s):  
S. Alley ◽  
C. Jennings ◽  
M. Duncan ◽  
S. Schoeppe ◽  
D. Gurtler ◽  
...  
Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Erica Schorr ◽  
Mary Whipple ◽  
Diane Treat-Jacobson

Introduction: Evidence supporting the effects of supervised exercise therapy (SET) on alleviating symptoms and improving walking ability for patients with symptomatic peripheral artery disease (PAD) is robust and well recognized. However, little is known about the impact of SET on free-living physical activity (PA). The aim of this study was to examine the relationship between participation in SET and changes in free-living PA among individuals in the the EX ercise Training to Reduce Claudication: Arm ER gometry versus T readmill Walking ( EXERT ) trial. Methods: In this randomized, controlled trial, 104 participants (mean age 68±9; 29% female) were allocated to receive treadmill (TM) exercise (n=41), upper body ergometry (UBE) exercise (n=42), or usual-care (UC) (n=21) for 12 weeks. Exercise participants attended SET three times per week; UC participants met with study staff weekly. PA was measured over 7 days via waist-worn ActiGraph accelerometers at baseline, 6, and 12 weeks. Steps per day was the primary outcome. Secondary outcomes were proportion of time in light and moderate to vigorous physical activity (MVPA), and sedentary time. PA was controlled for in TM participants by using SET logs. Results were analyzed using descriptive statistics, two-sample t-tests, and analysis of variance. Results: Regardless of randomization, average daily steps were low at baseline and 6 weeks (4,013 steps, p =.72; and 3,911 steps, p =.84, respectively), and slightly higher at 12 weeks (4,307 steps; p =.93). Although not statistically significant but perhaps clinically relevant, UBE participants exhibited greater increases in MVPA over 12 weeks (0.9% to 1.3%; F =.48, p =.62) compared to TM (1.2% to 1.3%; F =.35, p =.71) and UC (1.3% to 1.5%, F =.03, p =.97); similarly all participants exhibited reductions in sedentary time and increases in free-living PA between baseline and 12 weeks. Conclusions: These data suggest individuals with PAD attending SET replace sedentary time with light or moderate intensity PA regardless of exercise modality. Despite study participants meeting the recommended daily steps for adults with chronic conditions (3,500-5,500 steps), it is suspected that they did not reach the daily goal of 30 minutes of enhanced PA to reduce health risks. Future research should incorporate activity tracking devices that can provide feedback on PA as an approach to meet daily PA goals. Activity tracking devices used in conjunction with SET may further improve walking distance, symptom management, and quality of life among patients with symptomatic PAD.


2018 ◽  
Author(s):  
Iryna Sharaievska ◽  
Rebecca A Battista ◽  
Jennifer Zwetsloot

BACKGROUND Several studies support the impact of information communication technology–based interventions to promote physical activity among youth. However, little is known on how technology can be used by the entire family to encourage healthy behavior. Previous studies showed that children and youth rely and are dependent upon the decisions and values of their caregivers when it comes to having a healthy lifestyle. Thus, the exploration of behavior and attitudes of the entire family is needed. OBJECTIVE The study aimed to explore (1) perceptions of how the use of physical activity tracking devices (Fitbit Zip) by families in rural communities influence their patterns of participation in physical activity, (2) how attitudes toward physical activity change as a result of using physical activity tracking devices as a family, and (3) what factors influence participation in physical activity among families in rural communities. METHODS A total of 11 families with 1 to 3 children of different ages (7-13 years) took part in semistructured group interviews following 2 weeks of using physical activity tracking devices (Fitbit Zip) as a family. The participants were asked to discuss their experience using the Fitbit Zip as a family, the motivation to be physically active, the changes in their pattern of participation in those activities, the level of engagement by different family members, and the factors that affected their participation. All interviews were voice-recorded with the participants’ permission and later transcribed verbatim using pseudonyms. To analyze the data, the principal investigator (IS) used open, axial, and selective coding techniques. RESULTS A total of 3 themes and several subthemes appeared from the data. The families in rural communities reported no or minimal changes in physical activities as a result of using physical activity tracking devices (Fitbit Zip) because of a lack of interest or an already active lifestyle. However, the attitude toward physical activity was altered. The family members reported an increased awareness of their activity level, introduced more conversations about active and healthy lifestyles, and changed their view of physical activity to a more positive one. The participants described the changes they were able to make and the constraining factors that stopped them from making further changes in their lifestyle. CONCLUSIONS Technology might serve as a facilitator to participation in physical activity among families. Technology can motivate the change in attitude toward active recreation. As long-term changes in lifestyle require internal motivation, the change in the attitude might have a more long-lasting impact than the change in the immediate behavior. More longitudinal studies are needed to further explore long-term change in both behavior and attitude toward physical activity. Additional exploration of constraints to participation in physical activity among families is also an important area of exploration.


BMJ Open ◽  
2016 ◽  
Vol 6 (7) ◽  
pp. e011243 ◽  
Author(s):  
Stephanie Alley ◽  
Stephanie Schoeppe ◽  
Diana Guertler ◽  
Cally Jennings ◽  
Mitch J Duncan ◽  
...  

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Nada Elmagboul ◽  
Brian W. Coburn ◽  
Jeffrey Foster ◽  
Amy Mudano ◽  
Joshua Melnick ◽  
...  

2021 ◽  
pp. 135910532110082
Author(s):  
Erin K O’Loughlin ◽  
Catherine M Sabiston ◽  
Melissa L deJonge ◽  
Kristen M Lucibello ◽  
Jennifer L O’Loughlin

Whether physical activity (PA) tracking devices are associated with PA motivation in young adults is largely unknown. We compared total PA minutes per week, total minutes walking/week, meeting moderate-to vigorous PA guidelines, and past-year activity tracking across motivation cluster profiles among 799 young adults. Participants with “self-determined” profiles reported the highest total PA minutes/week followed by participants with “low intrinsic,” “controlled self-determined,” and “high external” profiles. A behavior regulation profile X activity tracking frequency interaction was not significant. Behavior regulation profiles may need to be considered in PA interventions using activity trackers.


2019 ◽  
Vol 76 (1) ◽  
pp. 258-284 ◽  
Author(s):  
Selen Razon ◽  
Alex Wallace ◽  
Jorge Ballesteros ◽  
Nicole Koontz ◽  
Lawrence W. Judge ◽  
...  

2016 ◽  
Vol 64 (1) ◽  
pp. 226-228 ◽  
Author(s):  
Francesca L. Tocci ◽  
Miriam C. Morey ◽  
Kevin M. Caves ◽  
Joi Deberry ◽  
Guy D. Leahy ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3757 ◽  
Author(s):  
Alejandro José Laguna Sanz ◽  
José Luis Díez ◽  
Marga Giménez ◽  
Jorge Bondia

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.


2018 ◽  
Vol 20 (3) ◽  
pp. 381-389 ◽  
Author(s):  
Gabrielle Turner-McGrievy ◽  
Danielle E. Jake-Schoffman ◽  
Camelia Singletary ◽  
Marquivieus Wright ◽  
Anthony Crimarco ◽  
...  

Background. Wearable physical activity (PA) trackers are becoming increasingly popular for intervention and assessment in health promotion research and practice. The purpose of this article is to present lessons learned from four studies that used commercial PA tracking devices for PA intervention or assessment, present issues encountered with their use, and provide guidelines for determining which tools to use. Method. Four case studies are presented that used PA tracking devices (iBitz, Zamzee, FitBit Flex and Zip, Omron Digital Pedometer, Sensewear Armband, and MisFit Flash) in the field—two used the tools for intervention and two used the tools as assessment methods. Results. The four studies presented had varying levels of success with using PA devices and experienced several issues that impacted their studies, such as companies that went out of business, missing data, and lost devices. Percentage ranges for devices that were lost were 0% to 29% and was 0% to 87% for those devices that malfunctioned or lost data. Conclusions. There is a need for low-cost, easy-to-use, accurate PA tracking devices to use as both intervention and assessment tools in health promotion research related to PA.


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